Breast cancer recurrence prediction with deep neural network and feature optimization

Breast cancer recurrence prediction with deep neural network and feature optimization

Breast cancer remains a pervasive global health concern, necessitating continuous efforts to attain effectiveness of recurrence prediction schemes. This work focuses on breast cancer recurrence prediction using two advanced architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit...

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Bibliographic Details
Main Authors: Arathi Chandran R I, V Mary Amala Bai
Format: Article
Language:English
Published: Taylor & Francis Group 2024-01-01
Series:Automatika
Subjects:
Breast cancer
recurrence
prediction
deep learning
LSTM
GRU
Online Access:https://www.tandfonline.com/doi/10.1080/00051144.2023.2293280

Internet

https://www.tandfonline.com/doi/10.1080/00051144.2023.2293280

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